Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of task scheduling the which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Resources for Peace? Managing Revenues from Extractive Industries in Post-Conflict Environments

Revenues from extractive sectors such as oil and gas, minerals, and logging play an important role in many post-conflict environments, often providing more than 30% of state fiscal receipts. When managed well, these revenues can help to finance postwar reconstruction and other vital peace-related needs. When mismanaged, however, resource revenues can undermine both economic performance and the quality of governance, thereby heightening the risk of renewed violence. This paper offers a number of proposals for managing revenues from extractive industries to better support peacebuilding.Extractive resources; oil revenues; peacebuilding; revenue-sharing

Democracy by Design: Perspectives for Digitally Assisted, Participatory Upgrades of Society

The technological revolution, particularly the availability of more data and more powerful computational tools, has led to the emergence of a new scientific field called “Computational Diplomacy”. Our work tries to define its scope and focuses on a popular subarea of it, namely “Digital Democracy”. In recent years, there has been a surge of interest in using digital technologies to promote more participatory forms of democracy. While there are numerous potential benefits to using digital tools to enhance democracy, significant challenges must be addressed. It is essential to ensure that digital technologies are used in an accessible, equitable, and fair manner rather than reinforcing existing power imbalances. This paper investigates how digital tools can be used to help design more democratic societies by investigating three key research areas: (1) the role of digital technologies for facilitating civic engagement in collective decision-making; (2) the use of digital tools to improve transparency and accountability in governance; and (3) the potential for digital technologies to enable the formation of more inclusive and representative democracies. We argue that more research on how digital technologies can be used to support democracy upgrade is needed. Along these lines, we lay out a research agenda for the future

Useful shortcuts: Using design heuristics for consent and permission in smart home devices

Prior research in smart home privacy highlights significant issues with how users understand, permit, and consent to data use. Some of the underlying issues point to unclear data protection regulations, lack of design principles, and dark patterns. In this paper, we explore heuristics (also called “mental shortcuts” or “rules of thumb”) as a means to address security and privacy design challenges in smart homes. First, we systematically analyze an existing body of data on smart homes to derive a set of heuristics for the design of consent and permission. Second, we apply these heuristics in four participatory co-design workshops (n = 14) and report on their use. Third, we analyze the use of the heuristics through thematic analysis highlighting heuristic application, purpose, and effectiveness in successful and unsuccessful design outcomes. We conclude with a discussion of the wider challenges, opportunities, and future work for improving design practices for consent in smart homes

Health Participatory Sensing Networks for Mobile Device Public Health Data Collection and Intervention

The pervasive availability and increasingly sophisticated functionalities of smartphones and their connected external sensors or wearable devices can provide new data collection capabilities relevant to public health. Current research and commercial efforts have concentrated on sensor-based collection of health data for personal fitness and personal healthcare feedback purposes. However, to date there has not been a detailed investigation of how such smartphones and sensors can be utilized for public health data collection. Unlike most sensing applications, in the case of public health, capturing comprehensive and detailed data is not a necessity, as aggregate data alone is in many cases sufficient for public health purposes. As such, public health data has the characteristic of being capturable whilst still not infringing privacy, as the detailed data of individuals that may allow re-identification is not needed, but rather only aggregate, de-identified and non-unique data for an individual. These types of public health data collection provide the challenge of the need to be flexible enough to answer a range of public health queries, while ensuring the level of detail returned preserves privacy. Additionally, the distribution of public health data collection request and other information to the participants without identifying the individual is a core requirement. An additional requirement for health participatory sensing networks is the ability to perform public health interventions. As with data collection, this needs to be completed in a non-identifying and privacy preserving manner. This thesis proposes a solution to these challenges, whereby a form of query assurance provides private and secure distribution of data collection requests and public health interventions to participants. While an additional, privacy preserving threshold approach to local processing of data prior to submission is used to provide re-identification protection for the participant. The evaluation finds that with manageable overheads, minimal reduction in the detail of collected data and strict communication privacy; privacy and anonymity can be preserved. This is significant for the field of participatory health sensing as a major concern of participants is most often real or perceived privacy risks of contribution

The experience of mental distress and recovery among people involved with the service user/survivor movement

This article examines how the personal experiences of mental distress of people involved in the British service user/survivor movement were shaped or transformed by this involvement, and the impact of involvement on their recovery journeys. The analysis was based on 12 in-depth interviews with service users/survivors who are, or were once, involved with the service user/survivor movement. Three large themes were identified regarding the ways in which social movement involvement affected the personal experience of mental distress: (a) making sense and reframing mental distress, (b) the social experience of involvement and (c) identity and identity reconstruction. We discuss how some features of the service user/survivor movement, such as self- help, user involvement, the centrality of experience to collective action, and the range of political positions adopted by activists can affect experience and recovery in different forms. As an exploratory study that looks into a complex topic, our findings illuminate the ways of surviving, recovering and experiencing mental distress in the context of a significant social movement

Development of an evolutionary game-theoretical model for trustworthy multi-channel information gathering and dissemination system framework among fisheries stakeholders

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyFisheries and its value added products contributes substantially in the socio-economic of developing countries including Tanzania. Researches shows that fisheries sector contributes 4.7% and 2.4% of the Gross Domestic Product (GDP) of Kenya and Tanzania respectively. Despite its huge contribution to socio-economic of the country, the Tanzania fisheries stakeholders remain challenged with limited access of fisheries information, knowledge, skills and new technologies. This challenges hinders the fisheries sector development and reduces income to stakeholders as well as the Government. This study investigated the fisheries information collecting and distribution among fisheries stakeholders in Mara and Mwanza regions of Tanzania. The study examined the channels owned and used by fisheries stakeholders to gather and disseminate fisheries information. Data were collected by administering a survey in four (4) districts purposively selected from the two regions and 400 respondents randomly selected was involved. The data were analyzed using python panda library and presented using bar and pie charts. Using the collected data, channel dissemination effectiveness probability of the six channels (short Message services, Cellular phone call, Television, Radio, mobile application, and Website) were calculated and comprehensive analysis performed using python plotly library. Furthermore, the study developed a multi channel fisheries information management system architectural framework and a participation reputation game based incentive mechanism namely EPRIGM to encourage the fisheries stakeholders donate truthful information and feedback. We modeled and simulated the dynamics of stakeholder’s strategy selection using replicator dynamic concept and derive the evolutionary stable strategies for the stakeholders. Results revealed that there is no single channel application that fits all stakeholders and that EPRIGM ensures truthful and honest stakeholders participation in gathering and disseminating fisheries information. In this study, we considered only seven parameters, namely channel coverage, listening ratio, watching ratio, channel access, average access time, information usefulness, and information sharing, in calculating channel effectiveness probability. Lastly, the empirical results of EPRIGM simulation revealed that all information users and information providers will choose honest strategy to capitalize on their earnings. We do recommend further studies to consider more factors like channel carrying capacity and channel costs in calculating channel effectiveness probability and consider application of EPRIGM in other domain of activities